


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


a       	=  R'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'







@add_start_docstrings(__magic_name__ )
class 							UpperCamelCase__ (						__magic_name__ ):
					__SCREAMING_SNAKE_CASE							:	Union[str, Any]            = 'rag'
					__SCREAMING_SNAKE_CASE							:	List[str]            = True

					def __init__(  self							: Tuple				,       UpperCamelCase__							: Dict=None				,       UpperCamelCase__							: str=True				,       UpperCamelCase__							: Union[str, Any]=None				,       UpperCamelCase__							: int=None				,       UpperCamelCase__							: List[str]=None				,       UpperCamelCase__							: Optional[int]=None				,       UpperCamelCase__							: List[str]=None				,       UpperCamelCase__							: Any=" / "				,       UpperCamelCase__							: Any=" // "				,       UpperCamelCase__							: str=5				,       UpperCamelCase__							: Union[str, Any]=300				,       UpperCamelCase__							: Dict=768				,       UpperCamelCase__							: List[str]=8				,       UpperCamelCase__							: str="wiki_dpr"				,       UpperCamelCase__							: Optional[Any]="train"				,       UpperCamelCase__							: int="compressed"				,       UpperCamelCase__							: Optional[Any]=None				,       UpperCamelCase__							: List[Any]=None				,       UpperCamelCase__							: Any=False				,       UpperCamelCase__							: int=False				,       UpperCamelCase__							: Any=0.0				,       UpperCamelCase__							: Union[str, Any]=True				,       UpperCamelCase__							: str=False				,       UpperCamelCase__							: int=False				,       UpperCamelCase__							: Any=False				,       UpperCamelCase__							: List[Any]=True				,       UpperCamelCase__							: Any=None				,       **UpperCamelCase__							: List[str]				,       ):






										'''simple docstring'''






										super().__init__(
										    bos_token_id=lowercase_				,       pad_token_id=lowercase_				,       eos_token_id=lowercase_				,       decoder_start_token_id=lowercase_				,       forced_eos_token_id=lowercase_				,       is_encoder_decoder=lowercase_				,       prefix=lowercase_				,       vocab_size=lowercase_				,       **lowercase_				,       )
										assert (
										    "question_encoder" in kwargs and "generator" in kwargs
										), "Config has to be initialized with question_encoder and generator config"
										lowercase_					=      kwargs.pop("""question_encoder"""							)
										lowercase_					=      question_encoder_config.pop("""model_type"""							)
										lowercase_					=      kwargs.pop("""generator"""							)
										lowercase_					=      decoder_config.pop("""model_type"""							)

										from ..auto.configuration_auto import AutoConfig

										lowercase_					=      AutoConfig.for_model(lowercase_				,       **lowercase_							)
										lowercase_					=      AutoConfig.for_model(lowercase_				,       **lowercase_							)

										lowercase_					=      reduce_loss
										lowercase_					=      label_smoothing
										lowercase_					=      exclude_bos_score
										lowercase_					=      do_marginalize

										lowercase_					=      title_sep
										lowercase_					=      doc_sep
										lowercase_					=      n_docs
										lowercase_					=      max_combined_length

										lowercase_					=      dataset
										lowercase_					=      dataset_split
										lowercase_					=      index_name

										lowercase_					=      retrieval_vector_size
										lowercase_					=      retrieval_batch_size
										lowercase_					=      passages_path
										lowercase_					=      index_path
										lowercase_					=      use_dummy_dataset

										lowercase_					=      output_retrieved

										lowercase_					=      do_deduplication

										lowercase_					=      use_cache

										if self.forced_eos_token_id is None:
															lowercase_					=      getattr(self.generator				,       """forced_eos_token_id"""				,       lowercase_							)

					@classmethod
					def 	UpperCAmelCase__							(  cls							: Dict				,       UpperCamelCase__							: PretrainedConfig				,       UpperCamelCase__							: PretrainedConfig				,       **UpperCamelCase__							: str							):






										'''simple docstring'''






										return cls(question_encoder=question_encoder_config.to_dict()				,       generator=generator_config.to_dict()				,       **lowercase_							)







					def 	UpperCAmelCase__							(  self							: Any							):






										'''simple docstring'''






										lowercase_					=      copy.deepcopy(self.__dict__							)
										lowercase_					=      self.question_encoder.to_dict()
										lowercase_					=      self.generator.to_dict()
										lowercase_					=      self.__class__.model_type
										return output



def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  if not isinstance(a__    ,     a__ ) or number < 0:
    raise ValueError("Input must be a non-negative integer" )

  _UpperCamelCase           =    0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
   import doctest

   doctest.testmod()




